Induction and evaluation of decision trees for lactation curve analysis

Machine learning has been identified as a promising approach to knowledge-based system development. This study focused on the use of decision-tree induction for knowledge acquisition to filter individual-cow lactations for group-average lactation curve analysis. Data consisted of 1428 cases, classified by a dairy-nutrition specialist as outliers (34 cases) or non-outliers. The classification performance was estimated through 10-fold cross validation. A relative operating characteristic curve was used to visualize the achievable range of trade-offs between correctly classifying positive and negative cases. A series of three final decision trees with increasing tendency of classifying a lactation as outlier was induced from the entire data set. For these trees, the expected true positive rates were 52, 68 and 92%, at false positive rates of 1.5, 3.5 and 8.6%, respectively. However, due to the low prevalence of outlier lactations (cases), this performance was associated with many false positives. The performance of individual decision nodes was tested against the entire data set to identify potentially counter-intuitive nodes resulting from overspecialization to the training data. The specialist reviewed the final trees and adjusted two decision nodes. This study suggests that although the input from a domain specialist remains important, decision-tree induction is a useful technique to support knowledge acquisition involved in the removal of outlier lactations.